Breast cancer risk assessment provides an opportunity to devise surveillance plans that may include enhanced screening for individuals at increased risk of breast cancer. Computerized analysis of mammographic parenchymal patterns may provide an objective and quantitative characterization and classification of these patterns, which may be associated with breast cancer risk. Computerized assessment of breast cancer risk that are based on the analysis of mammograms alone or combined with epidemiologic risk factors (for example, age) can serve as an alternative to current existing clinical methods, which are costly and/or information-dependent, in predicting breast cancer risk.
The breast is composed primarily of two components: fibroglandular tissue and fatty tissue. An average breast is comprised of 50% fibroglandular tissue and 50% fat. Fibroglandular tissue is a mixture of fibrous connective tissue and the glandular epithelial cells that line the ducts of the breast (the parenchyma).
Major breast diseases develop from the terminal ductal lobular units of the breast, and arise predominantly from the epithelial cells that line the ducts. However, the fibrous or connective tissue can also be involved. It is thought by some individuals that malignant breast disease develops through a process that starts with epithelial hyperplasia, i.e., an increase in the number of epithelial cells. Epithelial hyperplasia can progress to atypical hyperplasia in which the epithelial cells not only increase in number, but also change in a way that is not normal for these cells. The process, at this stage, is believed to be reversible. Once a certain criterion level of atypia is reached, the diagnosis of carcinoma-in-situ can be made, in which there is no invasion of malignant cells outside of the duct. The process of malignant transformation is considered irreversible at this stage. In the last phase of development, the cancer cells break out of the ductal walls and invade the surrounding stromal tissue, and at this point the disease is called infiltrating or invasive carcinoma.
Most (80%-85%) breast carcinomas can be seen on a mammogram as a mass, a cluster of tiny calcifications, or a combination of both. Other mammographic abnormalities are of lesser specificity and prevalence than masses and/or calcifications, and include skin or nipple changes, abnormalities in the axilla, asymmetric density, and architectural distortion.
Early detection of breast cancer can improve survival rates. Some statistics indicate that the overall five-year survival rate for women diagnosed with breast cancer is 84%, but when found at a small, localized stage, the 5-year survival rate is 97%. At least one study has shown that the use of screening mammography can reduce lesion size and stage at detection, improving the prognosis for survival. Currently, mammography is an established imaging technique for early detection of breast cancer. At least one organization has recommended annual screening mammography for all women over the age of 40.
U.S. Pat. No. 6,282,305 (Huo et al) is directed to a method and system for the computerized assessment of breast cancer risk, wherein a digital image of a breast is obtained and at least one feature is extracted from a region of interest in the digital image. The extracted features are compared with a predetermined model associating patterns of the extracted features with a risk estimate derived from corresponding feature patterns associated with a predetermined model based on gene carrier information or clinical information, or both gene carrier information and clinical information, and a risk classification index is output as a result of the comparison. Preferred features to be extracted from the digital image include 1) one or more features based on absolute values of gray levels of pixels in said region of interest, 2) one or more features based on gray-level histogram analysis of pixels in said region of interest; 3) one or more features based on Fourier analysis of pixel values in said region of interest; and 4) one or more features based on a spatial relationship among gray levels of pixels within the region of interest.
U.S. Pat. No. 5,984,870 (Giger et al.) is directed to a method for the analysis of a lesion existing in anatomical tissue, comprising the steps of (a) obtaining first digital image data derived from an ultrasound image of the anatomical tissue in which the lesion exists; (b) determining a location of the lesion in relation to the first digital data; (c) selecting for feature extraction analysis at least one of 1) a region of interest on the margin of the lesion, and 2) a region of interest which includes the lesion and a region of interest which is in the surrounding vicinity of the lesion, and 3) a region of interest which includes the lesion and a region of interest which is on the margin of the lesion; (d) extracting from each selected region of interest selected in said selecting step at least one first feature that characterize a lesion within said first image data; and (e) characterizing said lesion based on the extracted at least one first feature.
US Patent Applications No. 2003/0161513 and 2003/0125621 describe similar systems, using analysis of lesion shadows in an ultrasound image and a radial gradient index (RGI) feature in a sonographic image, respectively.
A difficulty associated with a computerized system for detecting and diagnosing breast lesions is segmentation of the lesion regions from the surrounding tissues. Some systems assume that segmentation is obtained by manual outlining the lesions using a graphic user interface, for example, U.S. Pat. No. 5,984,870 (Giger et al.). This manual procedure is labor-intensive, can disrupt full automation, and can be prone to human error, inconsistency and subjectivity. The resulting inaccuracy in the outline of the lesion has adverse effect on the subsequent computerized diagnosis because features computed from inaccurate lesion outline (e.g., those taught by U.S. Pat. No. 5,984,870 (Giger et al.)), tend to be inaccurate for lesion classification.
Accordingly, it is desirable to devise an automatic segmentation module to streamline the computerized mammography analysis system.
Pixel-based, edge-based, region-based, and model-based segmentation techniques are known in medical image processing. Each approach has its own limitations. For example, pixel-based segmentation techniques tend to have difficulties when there is a significant amount of noise in the image; edge-based techniques tend to experience problems when the boundary of the object is not well defined and when the image contrast is poor; while model-based techniques tend to fail when there is a significant amount of variation in the shape and appearance of the object of interest. Region-growing techniques require a good seed point (typically provided by manual interaction) and are subject to critical errors when adjoining objects closely match an object of interest in their appearance.
As such, there exists a need for a method which overcomes the limitations of existing methods.
The present invention provides a lesion segmentation and classification method wherein segmentation is automatic and relatively insensitive to variations in image noise and target appearance (color and shape). Further, the methods can provide a fully automatic system for segmenting and classifying lesions by cascading automatic lesion segmentation and automatic lesion classification.